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Fashion Finder: A System for Locating Online Stores on Instagram from Product Images

机译:Fashion Finder:一种从Product Images定位Instagram上在线商店的系统

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Searching for a fashion or clothing shops that carry either the piece that a consumer desires or a similar one can be troublesome due to lack of complete information, e.g., lack of the name of the brand or the location of a shop. A consumer may also waste a lot of time crawling in an online store. Many online stores utilises a social network called “Instagram” as a digital advertising platforms. In most of online stores, a user can simply use a keyword to search for items. Unfortunately, this cannot be done in Instagram. A consumer may search for a store on Instagram by using Instagram's ID assuming that the store is known. However, he or she needs to search each post one by one until he or she finds the desired product which may take a long time. A way to directly access these stores is needed. Therefore, we propose a platform called Fashion Finder that can assist consumers in their search for Instagram's online stores that sell their desired piece or an equivalent one. Fashion Finder uses a deep learning algorithm to be able to do so. It is simple to use, fast and a shop owner can add more items to the platform by themselves. The experiment shows that our proposed framework outperforms the conventional approach on the Colorful Fashion Parsing dataset.
机译:由于缺少完整的信息,例如缺少品牌名称或商店位置,寻找携带消费者期望的物品或类似物品的时装店或服装店可能会很麻烦。消费者还可能浪费大量时间在网上商店中爬行。许多在线商店都使用名为“ Instagram”的社交网络作为数字广告平台。在大多数在线商店中,用户只需使用关键字即可搜索商品。不幸的是,这无法在Instagram中完成。假定该商店已知,那么消费者可以使用Instagram的ID在Instagram上搜索商店。但是,他或她需要一个一个地搜索每个帖子,直到他或她找到所需的产品为止,这可能需要很长时间。需要一种直接访问这些商店的方法。因此,我们提出了一个名为“ Fashion Finder”的平台,该平台可以帮助消费者搜索出售所需商品或同等商品的Instagram在线商店。 Fashion Finder使用深度学习算法来做到这一点。它使用简单,速度快,并且店主可以自己向平台添加更多商品。实验表明,我们提出的框架优于Colorful Fashion Parsing数据集上的常规方法。

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